Software Development Lifecycle
The end-to-end process (requirements, design, build, test, deploy, monitor) for AI applications, incorporating governance and compliance checks at each stage.
A tailored SDLC for AI that adds governance checkpoints - impact assessments at requirements, security-by-design and privacy-by-design gates in design, QA and validation tests in build, policy enforcement in deployment, and ongoing monitoring post-launch. It ensures traceability of decisions, adherence to best practices, and alignment with regulatory frameworks throughout. Documentation at each stage supports audits and continuous improvement.
A financial-services firm extended its SDLC by adding: (1) a Data Privacy PIA at requirements; (2) a bias-mitigation design review; (3) automated security scans in CI; (4) policy-enforcement middleware at deployment; and (5) performance and compliance dashboards in production - ensuring every AI feature passes rigorous governance checks.
We help you find answers
What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
Who is Enzai built for?
How is Enzai different from other governance tools?
Can we start if we have no existing AI governance process?
Does AI governance slow down innovation?
How does Enzai stay aligned with evolving AI regulations?
Research, insights, and updates
Empower your organization to adopt, govern, and monitor AI with enterprise-grade confidence. Built for regulated organizations operating at scale.